class_docstr: Implementation of supervised GraphSAGE.
code: def predict(self):
      if self.sigmoid_loss:
          return tf.nn.sigmoid(self.node_preds)
      else:
          return tf.nn.softmax(self.node_preds)
  
file_path: GraphSAGE/graphsage/supervised_models.py
imports: import tensorflow
  import graphsage.models
  import graphsage.layers
  from graphsage.aggregators import MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, SeqAggregator, GCNAggregator
label_desc: Use image node_id 4 for calling the SupervisedGraphsage obj's underlying member method code with example usage: obj.predict() and returns: tf, tf